This paper introduces the RAG-RLRC-LaySum framework, designed to make complex
biomedical research understandable to laymen through advanced Natural Language
Processing (NLP) techniques. Our Retrieval Augmented Generation (RAG) solution,
enhanced by a reranking method, utilizes multiple knowledge sources to ensure
the precision and pertinence of lay summaries. Additionally, our Reinforcement
Learning for Readability Control (RLRC) strategy improves readability, making
scientific content comprehensible to non-specialists. Evaluations using the
publicly accessible PLOS and eLife datasets show that our methods surpass Plain
Gemini model, demonstrating a 20% increase in readability scores, a 15%
improvement in ROUGE-2 relevance scores, and a 10% enhancement in factual
accuracy. The RAG-RLRC-LaySum framework effectively democratizes scientific
knowledge, enhancing public engagement with biomedical discoveries.

该论文介绍了 RAG-RLRC-LaySum 框架，通过先进的自然语言处理技术，使复杂的生物医学研究能够被非专业人士理解。我们的 RAG 解决方案借助重新排序方法利用多个知识源来确保准确性和相关性，同时我们的 RLRC 策略通过提高可读性使科学内容易于理解。在使用公开可访问的 PLOS 和 eLife 数据集进行评估时，我们的方法超过了 Plain Gemini 模型，读者易读性得分提高了 20%，ROUGE-2 相关性得分提高了 15%，事实准确性提高了 10%。RAG-RLRC-LaySum 框架能够有效地推广科学知识，促进公众对生物医学发现的参与。

在 BioLaySumm 中的 RAG-RLRC-LaySum：整合了检索增强生成和可读性控制，用于医学文本的通俗摘要

RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented  Generation and Readability Control for Layman Summarization of Biomedical  Texts

Communication of scientific findings to the public is important for keeping
non-experts informed of developments such as life-saving medical treatments.
However, generating readable lay summaries from scientific documents is
challenging, and currently, these summaries suffer from critical factual
errors. One popular intervention for improving factuality is using additional
external knowledge to provide factual grounding. However, it is unclear how
these grounding sources should be retrieved, selected, or integrated, and how
supplementary grounding documents might affect the readability or relevance of
the generated summaries. We develop a simple method for selecting grounding
sources and integrating them with source documents. We then use the BioLaySum
summarization dataset to evaluate the effects of different grounding sources on
summary quality. We found that grounding source documents improves the
relevance and readability of lay summaries but does not improve factuality of
lay summaries. This continues to be true in zero-shot summarization settings
where we hypothesized that grounding might be even more important for factual
lay summaries.

科学发现向公众传播非常重要，但从科学文件生成易读的摘要具有挑战性。本文提出了一种简单的方法来选择基础知识来源并将其与源文件集成。使用 BioLaySum 汇总数据集我们发现，基础文献的添加可以提高摘要的相关性和可读性，但并不能提高摘要的事实准确性。

生物医学简介的可读性、相关性和真实性背景知识基础

Background Knowledge Grounding for Readable, Relevant, and Factual  Biomedical Lay Summaries

Summarisation of research results in plain language is crucial for promoting
public understanding of research findings. The use of Natural Language
Processing to generate lay summaries has the potential to relieve researchers'
workload and bridge the gap between science and society. The aim of this
narrative literature review is to describe and compare the different text
summarisation approaches used to generate lay summaries. We searched the
databases Web of Science, Google Scholar, IEEE Xplore, Association for
Computing Machinery Digital Library and arXiv for articles published until 6
May 2022. We included original studies on automatic text summarisation methods
to generate lay summaries. We screened 82 articles and included eight relevant
papers published between 2020 and 2021, all using the same dataset. The results
show that transformer-based methods such as Bidirectional Encoder
Representations from Transformers (BERT) and Pre-training with Extracted
Gap-sentences for Abstractive Summarization (PEGASUS) dominate the landscape of
lay text summarisation, with all but one study using these methods. A
combination of extractive and abstractive summarisation methods in a hybrid
approach was found to be most effective. Furthermore, pre-processing approaches
to input text (e.g. applying extractive summarisation) or determining which
sections of a text to include, appear critical. Evaluation metrics such as
Recall-Oriented Understudy for Gisting Evaluation (ROUGE) were used, which do
not consider readability. To conclude, automatic lay text summarisation is
under-explored. Future research should consider long document lay text
summarisation, including clinical trial reports, and the development of
evaluation metrics that consider readability of the lay summary.

本文是一篇关于使用自然语言处理技术生成简化版摘要的研究综述，发现基于 transformer 的方法如 BERT 和 PEGASUS 在生成 lay text summarisation 方面相对优秀，并建议采用抽取式和生成式相结合的混合方法，并开发一些新的评估指标以确保 lay summary 更易读。